Literature DB >> 24526443

Measurement of macular fractal dimension using a computer-assisted program.

George N Thomas1, Shin-Yeu Ong, Yih Chung Tham, Wynne Hsu, Mong Li Lee, Qiangfeng Peter Lau, Wanting Tay, Jessica Alessi-Calandro, Lauren Hodgson, Ryo Kawasaki, Tien Yin Wong, Carol Y Cheung.   

Abstract

PURPOSE: Macular diseases may be associated with an altered retinal vasculature. We describe and test new software for the measurement of retinal vascular fractal dimension to quantify the complexity of retinal vasculature at the macula (D mac) and to compare this with fractal dimension measured around the optic disc (D disc).
METHODS: A total of 342 macular-centered and optic disc-centered digital retinal photographs from 171 subjects was selected randomly from a population-based study. Retinal vascular fractional dimension (Df) was measured by two trained graders using a computer-assisted program (SIVA-FA, software version 1.0, National University of Singapore) on macula-centered (D mac) and optic disc-centered (D disc) photographs, to assess intergrader reliability. Measurements were repeated after two weeks to determine intragrader reliability. A separate 50 pairs of consecutively repeated images were selected and measured using SIVA-FA to assess intrasession reliability. Reliability analyses were conducted using intraclass correlation coefficients (ICC), and multiple linear regression analyses were performed to compare factors associated with D mac and D disc measurements.
RESULTS: The mean (SD) D mac and D disc values were 1.453 (0.060) and 1.484 (0.043), respectively, and were highly correlated (r = 0.70, P < 0.001). Intragrader, intergrader, and intrasession reliability for both Df measures was high (ICCs ranging from 0.88-0.99). In multiple regression analyses, age (both β = -0.03, P < 0.001) and hypertension (β = -0.02, P = 0.011; β = -0.02, P = 0.021, respectively) were independently associated with D mac and D disc.
CONCLUSIONS: The complexity of the retinal vasculature in the macula can be measured reliably and may be a useful tool to study parafoveal vascular networks in macula diseases, such as diabetic maculopathy.

Entities:  

Keywords:  fractal; macula; retinal vasculature

Mesh:

Year:  2014        PMID: 24526443     DOI: 10.1167/iovs.13-13315

Source DB:  PubMed          Journal:  Invest Ophthalmol Vis Sci        ISSN: 0146-0404            Impact factor:   4.799


  10 in total

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Authors:  Carol Yimlui Cheung; M Kamran Ikram; Ronald Klein; Tien Yin Wong
Journal:  Diabetologia       Date:  2015-02-11       Impact factor: 10.122

2.  The RETA Benchmark for Retinal Vascular Tree Analysis.

Authors:  Xingzheng Lyu; Li Cheng; Sanyuan Zhang
Journal:  Sci Data       Date:  2022-07-11       Impact factor: 8.501

3.  Using Retinal Imaging to Study Dementia.

Authors:  Victor T T Chan; Tiffany H K Tso; Fangyao Tang; Clement Tham; Vincent Mok; Christopher Chen; Tien Y Wong; Carol Y Cheung
Journal:  J Vis Exp       Date:  2017-11-06       Impact factor: 1.355

4.  Reproducibility of Retinal Microvascular Traits Decoded by the Singapore I Vessel Assessment Software Across the Human Age Range.

Authors:  Qi-Fang Huang; Fang-Fei Wei; Zhen-Yu Zhang; Anke Raaijmakers; Kei Asayama; Lutgarde Thijs; Wen-Yi Yang; Blerim Mujaj; Karel Allegaert; Peter Verhamme; Harry A J Struijker-Boudier; Yan Li; Jan A Staessen
Journal:  Am J Hypertens       Date:  2018-03-10       Impact factor: 2.689

5.  Distinguishing cognitive impairment by using singularity spectrum and lacunarity analysis of the retinal vascular network.

Authors:  Edmund Arthur; Gabor Mark Somfai; Maja Kostic; Susel Oropesa; Carlos Mendoza Santiesteban; Delia Cabrera DeBuc
Journal:  Neurophotonics       Date:  2019-09-23       Impact factor: 3.593

6.  Macular Vascular Geometry Changes With Sex and Age in Healthy Subjects: A Fundus Photography Study.

Authors:  Ziqing Feng; Gengyuan Wang; Honghui Xia; Meng Li; Guoxia Liang; Tingting Dong; Peng Xiao; Jin Yuan
Journal:  Front Med (Lausanne)       Date:  2021-12-15

7.  Evaluation of the Retinal Vasculature in Hypertension and Chronic Kidney Disease in an Elderly Population of Irish Nuns.

Authors:  Amy McGowan; Giuliana Silvestri; Evelyn Moore; Vittorio Silvestri; Christopher C Patterson; Alexander P Maxwell; Gareth J McKay
Journal:  PLoS One       Date:  2015-09-01       Impact factor: 3.240

8.  Quantitative Assessment of Retinopathy Using Multi-parameter Image Analysis.

Authors:  Zahra Ghanian; Kevin Staniszewski; Nasim Jamali; Reyhaneh Sepehr; Shoujian Wang; Christine M Sorenson; Nader Sheibani; Mahsa Ranji
Journal:  J Med Signals Sens       Date:  2016 Apr-Jun

9.  Dietary Patterns and Retinal Vessel Caliber in the Irish Nun Eye Study.

Authors:  C E Neville; S Montgomery; G Silvestri; A McGowan; E Moore; V Silvestri; C Cardwell; C T McEvoy; A P Maxwell; J V Woodside; G J McKay
Journal:  J Nutr Health Aging       Date:  2018       Impact factor: 4.075

10.  The Utility of Corneal Nerve Fractal Dimension Analysis in Peripheral Neuropathies of Different Etiology.

Authors:  Ioannis N Petropoulos; Abdulrahman Al-Mohammedi; Xin Chen; Maryam Ferdousi; Georgios Ponirakis; Harriet Kemp; Reena Chopra; Scott Hau; Marc Schargus; Jan Vollert; Dietrich Sturm; Tina Bharani; Christopher Kleinschnitz; Mark Stettner; Tunde Peto; Christoph Maier; Andrew S C Rice; Rayaz A Malik
Journal:  Transl Vis Sci Technol       Date:  2020-08-28       Impact factor: 3.283

  10 in total

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